Sustainable bioremadiation of Cd(II) in fixed bed column using green adsorbents: application of kinetic models and GA-ANN technique.

TLDR: Nag et al. as discussed by the authors used hybrid artificial intelligence GA-ANN for simulation and optimization of Cd(II) removal efficiency prediction as a function of influent variables, which performed appreciably well in terms of cross-correlation coefficient (R) value (between 0.997 to 0.999) and minimization of errors.

Abstract: This paper is continuation of our earlier paper in batch studies (Nag et al., 2018) and aims to deliver efficient and affordable solution for Cd(II) remotion from their wastewater of small to medium scale industries operating in India and worldwide. Three biowaste materials, jackfruit, mango and rubber leaves are used for Cd(II) bioremediation from synthetic wastewater in continuous down flow in packed bed columns. The influence of influent concentration (20–80 mg L−1), flow rate (10–25 ml min−1) and bed depth (3–9 cm) on Cd(II) removal has been examined at pH 6. Rise in bed height favoured the adsorption process whereas the decrease in bio-sorption efficiency was recorded at high influent flow rate and concentration. 98.26% Cd(II) was removed at breakthrough by jackfruit leaves at a flow rate of 10 ml min − 1 when the influent concentration was 20 mg L−1 and 5 cm bed height. Different kinetic models were evaluated for their comparative applicability. Applicability of hybrid artificial intelligence GA-ANN was attempted as a tool for simulation and optimization of Cd(II) removal efficiency prediction as a function of influent variables. The network performed appreciably well in terms of cross-correlation coefficient (R) value (between 0.997 to 0.999) and minimization of errors.